A comparison of species diversity index under different forest ecosystems in Thailand among this study and the others.
Climate change is a topic that has been widely discussed and debated over recent decades. Scientists have reached a general agreement that the lower atmosphere and the Earth’s surface are definitely getting warmer. The Intergovernmental Panel on Climate Change (IPCC) reported that a gradual but accelerating increase of atmospheric greenhouse gases has occurred since 1750 as result of human activities and among the anthropogenic greenhouse gases, CO2 is the most important. The global atmospheric concentration of CO2 has increased from a pre-industrial value of about 280 ppm to 379 ppm in 2005 (Alley et al., 2007). Temperature has risen by about 0.3-0.6oC since the late 19th century. If CO2 emissions were maintained at 1994 levels, its concentration would increase to about 550 ppm by the end of the 21st century (Chakraborty et al., 2000). Thailand is a member of the United Nation Framework Convention on Climate Change (UNFCCC), which is negotiated by the nations of the world in June 1992 (Michaelowa and Rolfe, 2001). The targets of the UNFCCC is to reducing CO2 emissions from the rate reported for 1990 during the five-year period from 2008 - 2012. This agreement is called the Kyoto Protocol which Thailand has ratified since August 28, 2002. There are two alternatives to reduce CO2, these include decreasing fossil fuel consumption and increasing carbon sink through forestry activities. According to Article 3.3 of the agreed Kyoto Protocol, some CO2 sources and sinks of forests shall be used to meet the commitments (UNFCCC, 1997). The sources and sinks to be used were measured as verifiable changes in carbon stocks in each commitment period (Terakunpisut et al., 2007; Forest research, 2011).
Forestry sectors are known as an important natural brake on climate change since they play an important role in the global both as a carbon sink and source because of their large biomass per unit area of land (Gibbs et al., 2007). The carbon in forests originates from the atmosphere and is accumulated in terms of the organic matter of soil and trees, and it continuously cycles between forests and the atmosphere through the decomposition of dead organic matter (Alexandrove, 2007). Thus, changing carbon stocks in forests can affect the amount of carbon in the atmosphere. If more carbon accumulates in forest through photosynthetic process, the forest will be a sink of atmospheric carbon. If the carbon stocks in forests decrease and release carbon into the atmosphere, the forests will become a source of atmospheric carbon. The carbon stocks of forests can change in two ways, on the one hand as a result of changes in forest area and on the other hand as a result of changes in carbon stocks on the existing forest area. Broadmeadow and Matthews (2003) report that approximately 1.6 GtC per year have released into the atmosphere as CO2 from deforestation during 1990s, but at the same time forest ecosystems is believed to have absorbed between 2 – 3 GtC per year.
Tropical forests have an importan role for carbon sequestration in a much higher quantity than any other biome (Gorte, 2009) and also as a main carbon source to the atmoshere in areas that have undergone deforestation or unsustainable management (Malhi et al., 2006). The amount of carbon storage in the world’s tropical forests which cover 17.6 x 106 km2 are approximately 4.28 x 1011 tonne C in vegetation and soils (Lasco, 2002). Figure 1 shows the total world’s tropical forests. In Asia, tropical forests are accounted for about 15.3 per cent in the world (UNCTAD Secretariat, n.d.). However, these forest ecosystems are facing the problem from deforestation and forest degradation in the tropics and Southeast Asia has been no exception. Lasco (2002) indicates that in 1990 deforestation rate in Southeast Asia was around 2.6x106 ha/ year. In addition there is liitle information on the carbon sequestration in natural forest ecosystems in Southeast Asia. To understand carbon sources and sinks, it is essential to estimate the biomass for these forests. Thus, the aim of this study was to estimate and compare the aboveground biomass and carbon stock between primary forest and secondary forest in the area of Khao Yai National Park.
2. Materials and methods
2.1. Study areas
This study was carried out at Khao Yai National Park. It covers a large complex area in Nakhon Ratchasima, Saraburi, Prachinburi and Nakhon Nayok Provinces.
This National Park is also part of the Dong Phaya Yen. The Dong Phayayen-Khao Yai Forest Complex is an important pool of biodiversity and complex terresstrial habitats not only in region, but also at global level. It was granted as a UNESCO Natural World Heritage Site on 14 July 2005 (Kekule, 2009). The climatological data was recieved from Khao Yai station, Department of Meteorology provied 25 years from 1982 – 2006. The annual temperature in the area varied from 30 - 33o C and the area recieved the annual mean precipitation of 1,123.48 ± 165.08 mm. The selected study areas were carried out in Nakhon Ratchasima Province as shown in Figure 2. The sites were selected based on anthropogenic disturbance. The primary forest was classified as non or least disturbed forested area and the main area characteristic was classified as the tropical rain forest. On the other hand, the secondary forest was disturbed from anthropogenic activities in the past and described as dry evergreen and mixed deciduous forest types. All sampling plots were in the permanent plot of Professor Emeritus Warren Y. Brockelman under the project : foraging and ranging behavior of gibbons in Khao Yai National Park.
2.2. Data collection and analysis
A randomly 1 ha sampling plot (100 m x 100 m) in each forest type was established. To reveal the tree composition and biomass, all live trees with a diameter ≥ 4.5 cm were recorded. The diameter was measured at breast height (DBH, 1.3 m height from the ground) to estimate biomass and the size class distribution of trees as well as species diversity in a sampling plot. All supported botanical data were represented by the species in terms of taxonomic classification identifie into Genera or Species, providing both local and scientific names by Aunttara Na-Thalang, a researcher at BIOTEC central research unit and a co-researcher of this project. In case of irregularities of trunk tree, the measurement was taken at the nearest lower point where the stem was cylindrical, or above the buttresses on large trunks. DBH was measured by used of diameter tape. Trees with multiple stems connected near the ground were counted as single individuals and bole circumference was measured separately. Tree height was recored by using a measuring pole. Figure 3 displayed primary data record and field measurement.
3. Data analysis
3.1. Species diversity and Important Value Index (IVI)
It was widely believe that species diversty related to the level of disturbance ( Mackey and Currie, 2001 ). Thus, species diversity was evaluated by using the Shannon – Wiener index method (see Equation 1) in this study to compare between primary forest and secondary forest. It was assumed that all species represented in the sampling plot were randomly sampled. In this method, the proportion of number of individuals of a species to the overall number of individuals in the sample plots was used to express the diversity of species in the studied ecosystem (Krebs, 1999).
To investigate the structural role of tree in the sampling plots, the importance value index (IVI) of each species was calculated using the percentage of relative abundance (R.A.), relative dominance (R.D.) and relative frequency (RF) (see Equation 5)
To test the significance of the difference between categories, one way analysis of variance (ANOVA) was carried out using the SPSS Statistics 17.0 software. Data on species distribution in two forest types were analyzed by correspondence analysis using the same software. We used correspondence analysis (CA) as the ordination method to examine the differences in the distribution of tree species using the same software.
3.2. Aboveground biomass and carbon sequestration
To estimate aboveground biomass in the study areas by non – destructive methods, we had to collect data such as diameter at breast height (DBH) and height of all trees. SILVIC Program was used to predict the mean total tree height in the sampling plots. It was developed from the relationship between DBH and tree height (Ht) by hyperbolic equation (see Equation 3) or D – H curve (Ogawa, Yoda and Kira, 1961). Forty trees in different sizes in the sampling plots were observed to analyse their height and DBH relationships. Ogawa (1969) showed that H was approximately equal to one for most mature forests. Assuming that h equaled one, the other coefficients, A and H* for each stand were calculated by using the non – linear least square method. These constant values were used to predict tree height in this study.
The next step was the aboveground biomass evaluation by non-destructive assessments. The biomass regression equations on the basis of DBH and Ht which derived from in tropical forests were applied for calculating the aboveground biomass and the size class analysis will evaluate the status of forest ecosystem. The primary forest used the equation developed by Tsutsumi et al. (1983) (see Equation 11) and the equation developed by Ogawa et al. (1965) was used for the secondary forest (see Equation 12).
Ws = stem mass(kg/ individual tree)
Wb = branches mass (kg/ individual tree)
Wl = leaf mass (kg/ individual tree)
Ht = height of tree (m)
DBH = diameter at breast height (cm)
Total carbon content was estimated from aboveground biomass by converted from biomass to carbon stock. From the reports (Atjay et al., 1979; Brown & Lugo, 1982; Iverson et al., 1994; Dixon et al., 1994; Cannell & Milne, 1995 and Terakunpisut et al., 2007), carbon content would be about fifty percent of the amount of aboveground biomass. To compare the potential of carbon sequestration between primary forest and secondary forest, frequency distribution of total aboveground biomass in a range of DBH size classes were considered to assess the potential of the forests across their size classes and age.
4. Results and discussion
4.1. Species diversity
Across sampling sites, tree species varried with forest types. Primary forest had greater species richness (75 species/ ha) than secondary forest (47 species/ ha). It probably implied that the study site in primary forest was more complexity in a community and species interaction. Since number of species compositions indicated the degrees of energy transfer through foodweb. In this case, the level of energy transfer in primary forest was stronger than secondary forest in order to support the higher total number of individuals of all species. This meant that the productivity in primary forest was also higher than another. In addition, the greater number of species compositions were most in ecosystems that have long time evolution, because organisms may develop mechanisms to conserve or more efficiently acquire any of the other limiting resources by certain physical or abiotic factors of the environment such as temperature, precipitation, light and soil.
From the species diversity (H´) measurement, The results showed that the overall plant species diversity of primary forest was higher than secondary forest, with the Shannon-Wiener indexes being 3.46 and 2.03 respectively. In practical, species diversity has been used to indicate the stability of the ecosystem. It meant that the high species diversity can exist in the spatially heterogeneous environment where the disturbances influence to the species in different degree. The species diversity index values measured and calculated from different forest ecosystems in Thailand had been listed and compared with this study as shown in Table 1. The species diversity values in primary forest and secondary forest were not much different from others study. The main conclusion was clearly demonstrated that the highest species diversity was from primary forest (tropical rain forest) because there were rich in resource such as diverse of habitat types and a large extent on food available in the tropical rain forest more than in other forest types.
|Forest ecosystem||Shannon – Wiener diversity index||References|
|The primary forest|
The secondary forest
|Tropical rain forest||3.48 - 3.52||Terakunpisut et al., 2007|
|Dry evergreen forest||3.62|
3.5 – 4.9
|Terakunpisut et al., 2007|
Sahunalu et al., 1979
|Mixed deciduous forest||3.09|
3.5 – 3.9
|Terakunpisut et al., 2007|
Sahunalu et al., 1979
This study also identified the dominant species according to the important value index (IVI). The result represented in Figure 4, which ranked from the highest value to lower value. The result indicated that common species in the primary forest were Ardisia nervosa (127 tree/ ha, IVI = 56.08) followed by Mastixia pentandra, Gonocaryum lobbianum, Dipterocarpus gracilis, Cinnamomum subavenium and Aglaia elaeagnoidea. The contribution of the dominant species in the secondary forest was Schima wallichii (505 trees/ ha, IVI = 71.94) and 2 co-dominant species were Machilus odoratissima and Eurya nitida.
The correspondence analysis revealed the pattern of the species distribution tree distribution in the study areas (see Figure 5). A correspondence map displayed two of the dimensions to relate the distribution of tree species with forest types. It showed that some plant species had high potential distribution. Thus, there were overlapped in their distribution between the different forest types. For example, Aquilaria crassna, Bridelia insulana, C. subavenium, Cleistocalyx operculatus, D. gracilis, Eurya nitida, Garcinia benthamii, G. lobbianum, Helicia formosana, Ilex chevalieri, Litsea umbellata, M. pantandra, Phoebe lanceolata, Syzygium grande, S. siamensis and S. Syzygiodes occurred in both forest types and the pattern indicated links to both forests. Because of the similarity of climate such as annual precipitation and annual temperature, the species compositions of each forest type had features in common and only a few rare species were specific to a single forest type. The analysis of variance showed that tree species did not significantly differ across the two forest types in terms of species richness, F (1, 120) = 2.328, p = 0.130. This was due to several species were found in both forests.
Figure 6 showed the DBH size class distribution on two sampling plots. The density of plants with DBH ≥ 4.5 cm in secondary forest was 1,249 trees/ ha due to lots of small tree sizes. While tree density in primary forest was only 919 trees/ ha since the main tree size class in this area was medium to large tree sizes at DBH > 40 – 60 cm and 60 – 80 cm. It was cler that the frequency distribution curves of DBH were all L- shaped in both forests. The density of trees was the highest at the left end of the graph and decreased afterward. Up to > 20 – 40 cm, the distribution curves of primary forest and secondary forest were similar, although the amount of trees in secondary forest were much higher, especially in DBH size class ≥ 4.5 – 20 cm. The main differences between primary forest and secondary forest were in the number of trees in medium size class at DBH > 40 – 60 cm and > 60 – 80 cm which were greater amount in primary forest. The analysis of variance showed that there was significant difference of tree density between primary forest and secondary forest, F (1, 120) = 4.393, p = 0.038.
4.2. Aboveground biomass and carbon sequestration
Aboveground biomass distribution and carbon storage in different DBH size classes were compared between primary forest and secondaryforest in Khao Yai National Park (see Figure 7). It was remarkable that total aboveground biomass accumulation in primary forest (684.76 tonne/ ha) was higher than seconday forest (198.20 tonne/ ha). Although the number of trees were significantly greater in secondary forest, but the highest tree density were in the group of small tree size classes at DBH ≥ 4.5 – 20 and 20 – 40 cm which had lowest individual volume and biomass. On the other hand, the most aboveground biomass accumulation was found in big trees of size class at > 60 – 80, > 80 –100 and > 100 cm that were dominant tree groups in primary forest. Because these trees were highest stem volume and large diameter, although they were the smallest group of tree densities. The analysis of variance revealed a significant difference in terms of median total aboveground biomass between primary forest and secondaryforest, F (1, 3046) = 29.189, p = 0.000.
In comparison with other tropical forests, the range of aboveground biomass in this study both areas were similar (see Table 2). The result in Primary forest was compared to tropical rain forest, while data in secondary forest was compared with the biomass in dry evergreen forest and mixed deciduous forest.
|Forest ecosystem||Aboveground biomass (tonne/ ha)||References|
|The primary forest|
Tropical rain forest
Yamakura et al., 1986
|The secondary forest|
Dry evergreen forest
Dry evergreen forest
Mixed deciduous forest
73.06 - 173.10
Mani and Parthasarathy, 2007
Terakunpisut et al., 2007
Terakunpisut et al., 2007
The percentage data of tree density and carbon sequestration were presented in Table 3. The total carbon sequestration in primary forest and secondary forest were equal to 342 and 99.10 tonne C/ ha, respectively. The results showed that the distribution of DBH size classes and the total carbon storage in each size class varied between the forest types. About 80 per cent of the carbon stock was presented in DBH size class at ≥ 4.5 – 20 cm and > 20 – 40 cm in secondary forest but contributed only 20 per cent of total carbon stock in primary forest. The carbon storage was highest in DBH size class at > 60 – 80 cm and > 80 – 100 cm in primary forest.
However, the highest potential size class to sequester CO2 from the atmosphere in primary forest and secondary forest were DBH size class at > 60 – 80 cm and > 20 – 40 cm, respectively. Since number of trees in these size classes were lower than other, but the amount of carbon storage were greater than other groups which had higher tree density. For example, in secondary forest; trees in the size class at ≥ 4.5 – 20 cm were five times more tree density than trees in the size class at > 20 – 40 cm, but the amount of carbon storage were similar. Likewise primary forest, trees in the size class at > 60 – 80 cm were found only 0.44 per cent, but the amount of carbon storage was nearly four times of trees in the size class at ≥ 4.5 – 20 cm.
|size class||The primary forest||The secondary forest|
|(DBH, cm)||Tree density (℅)||Carbon stock (℅)||Tree density (℅)||Carbon stock (℅)|
|≥ 4.5 - 20||76.20||6.93||85.30||38.18|
|"/ 20 - 40||20.00||13.43||14.37||39.96|
|"/ 40 - 60||3.04||6.37||0.19||1.48|
|"/ 60 - 80||0.44||26.73||0.05||0.62|
|"/ 80 - 100||0.33||46.53||-||-|
In summary, the distribution pattern of aboveground biomass had been related to past disturbance history the forests. Total aboveground biomass in the primary forest was about triple that of the secondary forest. However, both study areas had high carbon sequestration potential in the future due to presence of large number of trees belonging to small DBH size classes. These trees in size class at ≥ 4.5 – 20 cm were in the youth phase and their growth rate was accelerating to reach maturity. It meant that at the present these smaller trees are not the highest carbon sequestration potential, but in the near future they can sequester CO2 from the atmosphere through photosynthesis to form their structure till senescent phase. Broadmeadow and Matthews (2003) suggested the option to reserve carbon in the forests by minimal intervention, with a gradual long – term increase in carbon stocks.
The number of tree species occurring on the sample area in the primary forest and the secondary forest were 75 and 47 species, respectively. To conclude the correspondence analysis and ANOVA, it was found that there were many species in common between primary forest and secondary forest. So each forest type had not a distinctive of species distribution. From the results, it was found that the tree density was counted in the secondary forest as 2,129 trees/ ha due to lots of saplings and small trees, while the tree density in the primary forest was found only 919 trees/ ha since the main tree size class in this area was medium to large tree size at > 60 – 80 cm.
The primary forest and secondary forest of Khao Yai National Park had carbon stocks 342.29 and 99.10 tonne C/ ha, respectively. The total aboveground carbonstorage in the primary forest was significantly greater than the secondary forest. Although the young trees belonging to the size class at DBH ≥ 4.5 - 20 cm dominated both forests in terms of tree density, the carbon sequestration potential was greater in the size class at DBH > 20 - 40 cm in secondary forest and in the size class at DBH > 60 - 80 cm in primary forest. Both forests were very important for carbon sequestration because there were typically high carbon stocks. Moreover, the result also implied that the potential was considerably high to sequester carbon in both forest areas in the near future due to lots of small trees in the areas. We hope that the results of this study on aboveground biomass and carbon sequestration will be useful to conserve these forest areas under sustainable management.
The authors express their sincere gratitude to Kasetsart University Research and Development Institute (KURDI) for financial support of this project and wish to thank Kasetsart University for support in publishing. The authors also thank the Biodiversity Research and Training Program (BRT) to support young sciencetists. The authors are thankful to Professor Emeritus Warren Y. Brockelman, for his support valuable data use in this article and giving permission to carry out field work in the permanent plot under the project: foraging and ranging behavior of gibbons in Khao Yai National Park. The author also thanks the staff of Professor Emeritus Warren Y. Brockelman’s project and a team of undergraduade students from the Faculty of Liberal Arts and Science, Kasetsart University for help in the field survey. A big thank you also goes out to Dr. Taeng-on Prommi, a lecturer at Faculty of Liberal Arts and Science for help in the application process of publishing grant. Also thank Megan Combs for always improving the language of this paper.